System, user interface and method for interactive negative explanation of machine-learning localization models in health care applications

ABSTRACT

A method and system for assessing a machine learning model providing a prediction as to the disease state of a patient from a 2D or 3D image of the patient or a sample obtained therefrom. The machine learning model produces a prediction of the disease state from the image. The method involves presenting on a display of a workstation the image of the patient or a sample obtained therefrom along with a risk score or classification associated with the prediction. The image is further augmented with highlighting to indicate one or more regions in the image which affected the prediction produced by the machine learning model. Tools are provided by which the user may highlight one or more regions of the image which the user deems to be suspicious for the disease state. Inference is performed on the user-highlighted areas by the machine learning model. The results of the inference are presented to the user via the display.

BACKGROUND

This disclosure relates to a system, user interface, and method forinteractive assessing of negative predictions generated by machinelearning localization models. The teachings of this disclosure haveapplications in various fields, including in machine learning healthcare applications, such as examination of microscope slides, diagnosisof breast cancer in mammograms, or other types of cancer in otherradiology modalities (e.g., X-ray, CT, MRI), photographic images(dermatology) and still others. The teachings also have applications inother areas, such as metallurgy, parts inspection, semiconductormanufacturing, and others, where a machine learning localization modelis making a prediction based on an input image data set, the predictionis negative, and the user seeks to query the model further.

The use of machine learning models for several health care applicationsis described in the patent and technical literature. In one example,such models are developed to assist a pathologist in identifying thepresence of disease in a 2D or 3D volumetric image of the patent orspecimen derived from the patient. For example, the pathologist may betrying to determine if tumor cells (i.e., cancer) are present in amagnified digital image of tissue, such as for example lymph nodetissue, breast or prostate cancer tissue obtained from a biopsy. Asanother example, a machine learning model may assist a radiologist indetecting cancerous cells in a mammogram or chest X-ray. The machinelearning models are trained to recognize cancerous cells or tissue froma set of training data (image sets), typically using convolutionalneural networks or other classification procedures which are known inthe art.

Various techniques and tools are known which address the problem of“model explanation.” Model explanation is a process of justifying, in ahuman-readable manner, why a machine-learning model made a certainrecommendation (e.g. diagnosed a patient with cancer). Deep-learningmodel predictions are notoriously difficult to explain. This istolerable in use cases such as YouTube video rankings, but completelyunacceptable for use cases in high impact applications such as medicine.Pathologists, and other medical professionals, prefer to know not onlywhat the model prediction is, but also why it is so, in order to haveconfidence in the prediction.

Researchers for the present assignee have developed some basic methodsfor explaining a model prediction. For example, if a sample or image isdiagnosed as “positive” (e.g. has cancer, or high likelihood of cancer),the following methods have been used: (1) a bounding box around asuspected lesion as produced by a detection model and later classifiedby a classification model is presented to the user, example shown inFIG. 1A; (2) “heat maps”, typically with color coding to show degree ofconfidence of likelihood of particular regions or pixels beingcancerous, are presented, example shown in FIG. 1B, seehttps://ai.googleblog.com/2017/03/assisting-pathologists-in-detecting.htmland (3) attention masks are presented to the user, an example of whichis shown in FIG. 10.

Despite these advances, options for explaining the lack of a finding(e.g. no cancer) are limited, as it is hard to prove a negative. Wthmost computer-aided detection systems, a medicalprofessional/pathologist/radiologist who believes that a certain regionof interest is suspicious of a disease has no way of knowing whether themodel producing a negative prediction missed that region or whether themodel examined the region and classified it as normal/benign. Due tolimited computational resources, in some implementations of machinelearning in this domain, a detection model is used initially to findsuspected cancerous tissue and only those regions found by the detectionmodel are subsequently classified with a classification model.Accordingly, there is some risk that the detection model may have missedan areas that is potentially cancerous and that therefore the overallresulting prediction of “negative” may not be correct.

This problem of model explanation, in the context of a “negative”prediction, has led many Computer Aided Detection/Diagnosis (CAD)systems existing on the market to fail to deliver improved results. Forexample, mammography CAD systems have been shown to decreasespecificity, partly because such systems employ user interfaces that,while they alert the radiologist with a multitude of findings, they failto assure the radiologist that findings which the radiologist identifiedthemselves as suspicious were deemed benign by the machine learningmodel. This disclosure addresses this unmet need.

SUMMARY

In one aspect, a method is disclosed for assessing machine learningmodel providing a prediction as to the disease state of a patient from2D or 3D imagery, e.g., an X-ray, CT scan, pathology specimen, of thepatient or a sample obtained therefrom. The machine learning model istrained to make a prediction from the 2D or 3D imagery, e.g., cancerous,benign, calcification, lesion, etc. The method includes steps of: a)presenting an image with a risk score or classification associated withthe prediction, wherein the image is further augmented with highlightingto indicate one or more regions in the image which affected theprediction produced by the machine learning model; b) providing a userinterface tool for highlighting one or more regions of the image, c)receiving a user input highlighting one or more regions of the image; d)subjecting the highlighted one or more regions to inference by themachine learning model; and e) presenting the results of the inferenceon the one or more regions to the user via the display.

In another aspect, a workstation is described which is configured toassess a machine learning model providing a prediction of a patient from2D or 3D imagery. The workstation includes a) display for displaying theimage of the patient or a sample obtained therefrom along with a riskscore or classification associated with the prediction, wherein theimage is further augmented with highlighting to indicate one or moreregions in the image which affected the prediction produced by themachine learning model; and b) a user interface tool by which the usermay highlight on the display one or more regions of the image which theuser deems to be suspicious for the disease state, wherein the userinvokes the tools to thereby highlight the one or more regions. Thedisplay is further configured to present the results of inferenceperformed by the machine learning model on the one or more regionshighlighted by the user.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a mammogram with a bounding box around a suspected lesion asproduced by a detection model and later classified by a classificationmodel.

FIG. 1B is CT scan with hatching corresponding to colored areas forminga “heat map” indicating areas of high likelihood of cancer.

FIG. 1C is retinal photograph with an attention mask (area highlightedwith solid lines) indicating those portions of the retinal photographwhich contributed to model prediction of a particular eye disease.

FIG. 2 is a mammogram containing both a risk score (cancer score, =0.6%,likely benign, in this example) along with an overlay in the form of arectangle identifying a region that affected the machine learningmodel's diagnostic prediction.

FIG. 3 is an illustration of the mammogram of FIG. 2 but with the userhaving drawn on the mammogram a new region which the user findssuspicious and requests additional findings. The region could be drawnwith the aid of a cursor, or finger or a pen if the mammogram ispresented on a touch-sensitive display; the manner in which the regionis identified is not particularly important.

FIG. 4 is an illustration of the mammogram of FIG. 3, after the machinelearning model has performed inference on the region highlighted by theuser in FIG. 3. The display shows the classification for theuser-identified region along with a local region risk score, in thiscase cancer score of 0.02%. The results of inference may include localregion score, classification characteristics, regressed values and otherfeatures useful in the interpretation of the region. Such results arepresented to the user along with optionally an updated risk score forthe overall case. The process the user identifying and highlighting newregions as per FIG. 3, performing inference and presenting results asper FIG. 4 can continue as needed by the user.

FIG. 5A is an illustration of a tissue image containing both a riskscore along with an overlay in the form of a rectangle identifying acluster of cells that affected the machine learning model's diagnosticprediction.

FIG. 5B is an illustration of the tissue image of FIG. 5A but with theuser having drawn on the tissue image a new region which the user deemssuspicious and requests additional findings. The results of theinference performed on the region drawn by the user are presented on thedisplay.

FIG. 5C is an illustration of the tissue image of FIG. 5A with the userhaving drawn on the image another new region which was deemedsuspicious. The results of the inference performed on the new region arepresented on the display.

FIG. 6 is an illustration of one example of a computing environment inwhich the methods of this disclosure can be practiced.

FIG. 7 is a flow chart showing one embodiment of the processingperformed in the computing environment of FIG. 6 to perform the method.

DETAILED DESCRIPTION

In one aspect, a method is disclosed for assessing, i.e., facilitatinghuman understanding, of a machine learning model providing a predictionas to the disease state of a patient from a 2D or 3D image of thepatient or a sample obtained therefrom. An example of a 2D image wouldbe a radiology image, such as chest X-ray, or mammogram, or magnifieddigital image of a pathology specimen. A 3D volumetric image could takethe form of a CT scan, nuclear magnetic resonance, or other. In oneaspect, this disclosure relates to model interpretability in thesituation when the machine learning model produces a negative predictionof the disease state from the image, for example a prediction of“benign” or “low confidence” in the presence of cancer cells in the 2Dor 3D image. The threshold at which the prediction is deemed “negative”is not particularly important and can vary depending on such matters asthe model sensitivity or user preferences. In the following discussion,the numerical values of cancer score or local region scores arehypothetical and offered only by way of example to illustrate the coreideas of this disclosure and may or may not reflect actual scoringregimes of a given patient sample and machine learning model.

The method includes a first step of presenting on a display of aworkstation an image of the patient or a sample obtained therefrom(e.g., mammogram, or magnified tissue image) along with a risk score orclassification associated with the prediction. The image is furtheraugmented with highlighting to indicate one or more regions in the imagewhich affected the prediction produced by the machine learning model,such as cluster of cells. An example of this is offered by way ofillustration and not limitation in FIG. 2, which is a mammogram 10 of apatient containing both a risk score 12 (cancer score, =0.6%, likelybenign, in this example) along with an overlay in the form of arectangle 14 identifying regions that affected the machine learningmodel's diagnostic prediction. In this particular example, a machinelearning model includes a detection model which is trained to detectlikely cancer candidates and the bounding box or rectangle 14 surroundsa candidate. The machine learning model performed inference on thecluster or cells detected by the detection model and they scored “low”and unlikely be cancerous, therefore the overall assessment for thesample is “negative”, indicated by the score of 0.65 percent in thisexample.

The method includes a step of providing a user interface tool by whichthe user may highlight one or more regions of the image, e.g., which theuser deems to be suspicious for the disease state or wishes to query themachine learning model, and receiving an input highlighting the one ormore regions. The tool could consist simply of a mouse associated withthe display. Alternatively, the display is touch sensitive and the toolstake the form of known graphics processing software which recordspositions on the display which are touched by the user (directly orindirectly, e.g., with a pen) and translates such positions to locationswithin the image. The manner in which the user highlights the one ormore regions is not particularly important and can vary. An example ofthis step is shown in FIG. 3, which is an illustration of the mammogramof FIG. 2 but with the user having drawn on the mammogram a region 20with the mouse which the user finds suspicious and requests additionalfindings. The region could be drawn with the aid of a cursor, or fingerif the mammogram is presented on a touch-sensitive display; the mannerin which the region is identified is not particularly important. Onepossible method for drawing a bounding box around the area of interestis described in U.S. Pat. No. 10,013,781 to Christopher Gammage et al.,assigned to the assignee of this invention.

The method continues with a step of subjecting the highlighted one ormore regions (20 in FIG. 3) to inference by the machine learning model.In this step, the machine learning model processes the pixel data in theregion highlighted by the user and generates an output, typically aclassification result, or prediction, or score, depending on how themodel is configured. In this example, the machine learning model maytake the form of a convolutional neural network which is trained fromthousands of healthy and cancerous mammograms in order to correctlyclassify or score new (previously unseen) examples. Deep convolutionalneural network pattern recognizers, of the type described here, arewidely known in the art of pattern recognition and machine vision, andtherefore a detailed description thereof is omitted for the sake ofbrevity. The Inception-v3 deep convolutional neural networkarchitecture, which is one possible implementation, is described in thescientific literature. See the following references, the content ofwhich is incorporated by reference herein: C. Szegedy et al., GoingDeeper with Convolutions, arXiv:1409.4842 [cs.CV] (September 2014); C.Szegedy et al., Rethinking the Inception Architecture for ComputerVision, arXiv:1512.00567 [cs.CV] (December 2015); see also US patentapplication of C. Szegedy et al., “Processing Images Using Deep NeuralNetworks”, Ser. No. 14/839,452 filed Aug. 28, 2015. A fourth generation,known as Inception-v4 is considered an alternative architecture. See C.Szegedy et al., Inception-v4, Inception-ResNet and the Impact ofResidual Connections on Learning, arXiv:1602.0761 [cs.CV] (February2016). See also US patent application of C. Vanhoucke, “ImageClassification Neural Networks”, Ser. No. 15/395,530 filed Dec. 30,2016. The description of the convolutional neural networks in thesepapers and patent applications is incorporated by reference herein. Theuse of “attention” models, and related techniques, such as integratedgradients, is described in the scientific and patent literature. D.Bandanau et al., Neural Machine Translation by Jointly Learning to Alignand Translate, January 2014 (arXiv:1409.0473[cs.CL]. Choi et al., GRAM:Graph-based attention model for Healthcare Representation Learning,arXiv:1611.07012v3 [cs.LG] April 2017; Choi et al., RETAIN: anInterpretable Predictive Model for Healthcare using Reverse TimeAttention Mechanism, arXiv:1608.05745v3[cs.GL] February 2017. M.Sundararajan et al., Axiomatic Attribution for Deep Networks,arXiv:1703.01365 [cs.LG] (June 2017). Several papers are directed tolung module detection and diagnosis from CT scans using deep learning,including Xiaojie Huang, et al., Lung Nodule Detection in CT Using 3DConvolutional Neural Networks, The 2017 IEEE International Symposium onBiomedical Imaging, April 2017; Francesco Ciompi et al., Towardsautomatic pulmonary nodule management in lung cancer screening with deeplearning, Scientific Reports 7, article no. 46479 Apr. 17, 2017; WenqingSun et al., Computer Aided lung cancer diagnosis with deep learning,Medical Imaging 2016, Proc. of SPIE vol. 9785 (March 2016); Albert Chonet al., Deep Convolutional Neural Networks for Lung Cancer Detection,Stanford University Reports (2017),www.cs231n.stanford.edu/reports/2017/pdfs/518.pdf, and Wafaa Alakwaa, etal., Lung Cancer Detection and Classification with 3D ConvolutionalNeural Network (3D-CNN), International Journal of Advanced ComputerScience and Applications, vol. 8 no. 8. pp 409-417 (2017).

The method continues with a step of presenting the results of theinference on the one or more regions (20 in FIG. 3) to the user via thedisplay. An example of this is shown in FIG. 4, which is an illustrationof the mammogram of FIG. 3, after the machine learning model hasperformed inference on the region 20 highlighted by the user in FIG. 3.The display shows the results of the inference at 22 as taking the formof a classification for the user-identified region 20 (“benigncalcification”) along with a local region risk score, in this casecancer score of 0.02%. The results of inference that may include localregion score, classification characteristics, regression values and/orother features useful in the interpretation of the region 20, which arepresented to the user. Optionally, the results 22 are presented alongwith an updated risk score for the overall case, shown at 24. In thisparticular instance, the overall case risk score increased from 0.65% to0.67%.

Note: in this example, case level probability increases with everynon-zero risk lesion found and decreases with every area examined (wecan apply a lower penalty for known unknowns). Which of these effects isstronger depends on how large an area was examined and how serious alesion was discovered. An alternative numerical example for FIGS. 3 and4 would be as follows

-   -   model found one lesion, cancer score 0.6%, overall case score        1.1% (to allow for uncertainty in regions that were not closely        examined)    -   an operator is concerned about another lesion, highlights it,        and asks the model to perform inference on it    -   model classifies that lesion as benign with cancer score of 0.2%        and increases the case cancer score to 1.2% since that region        was not considered earlier, but the case continues to be        considered benign. Note that the increase in case score needs to        be diminishing with the amount of regions examined in order to        be intuitive, as otherwise having a lot of regions examined        would lead every case to eventually become “positive.”

The process the user identifying new regions as per FIG. 3, performinginference and presenting results as per FIG. 4 can continue as needed bythe user.

As noted earlier, the methods of this disclosure are suitable for usewith tissue pathology samples, for example image data in the form ofmagnified digital images of prostate tissue. The methodology will beexplained in this context in conjunction with FIGS. 5A-5C. Inparticular, FIG. 5A is an illustration of a magnified tissue imagecontaining both a risk score (cancer score, =0.40%, benign, in thisexample) along with an overlay in the form of a rectangle 30 identifyinga cluster of cells that affected the machine learning model's diagnosticprediction. In this example, the overall specimen was predicted by themachine learning model to be benign, with a risk score of 0.40%. Thisrisk score and the bounding box 30 are presented to the user on thedisplay of a workstation as shown.

The pathologist viewing the magnified digital image of the slide on thedisplay of a pathology workstation determines that there are other areasof potential interest in the slide which are not within the bounding box30, and therefore may wish to know if such other areas are potentiallycancerous. Therefore, as shown in FIG. 5B, the user has drawn on thetissue image with user interface tool (e.g., a mouse and graphicsprocessing software) a new region 40 which the user deems suspicious andrequests additional findings from the machine learning model. The pixeldata associated with the region 40 are provided as input to the machinelearning model. The results of the inference performed on the region 40drawn by the user are presented on the display, as indicated at 42.These results include a local region score (0.20%), a classificationresult (negative) and optionally regression values or other information.An updated overall cancer score for the sample is generated anddisplayed, in this example 0.42%. In this example, the classification ofthe additional cluster of cells within the box 40 has caused the overallcancer score to increase from 0.40 percent to 0.42 percent.

In this example, the user has elected to highlight still another area ofthe tissue specimen for further scoring/inference by the machinelearning model. In this example, as shown in FIG. 5C, the user has drawnon the image another new region 50 which was deemed suspicious. Theresults of the inference performed on the new region are presented onthe display as indicated at 52.

Note that in this example, there may be intermediate steps such aszooming and panning to new locations within a given tissue image but theprocess described above in FIGS. 5A-5C is essentially unchanged. Theuser selection of new areas as indicated in FIG. 5C can of coursecontinue until the user is satisfied with the negative model predictionfor the tissue specimen.

Referring now to FIG. 6, the method of this disclosure can beimplemented in a workstation 100 which is configured to facilitate humanunderstanding of a machine learning model providing a prediction as tothe disease state of a patient from a 2D or 3D image of the patient or asample obtained therefrom. The machine learning model produces anegative prediction of the disease state from the image, e.g., by way ofa classification result or score which is presented to the user. Theworkstation includes a) display 102 for displaying the image of thepatient or a sample obtained therefrom along with a risk score orclassification associated with the prediction. The image is furtheraugmented with highlighting to indicate one or more regions in the imagewhich affected the negative prediction produced by the machine learningmodel (see FIGS. 3 and 5B). The workstation includes a user interfacetool, e.g., mouse 104, or touch sensitive display, by which the user mayhighlight on the display one or more regions of the image which the userdeems to be suspicious for disease. The user invokes the tools, e.g.,mouse, to thereby highlight the one or more regions. The display isfurther configured to present the results of inference performed by themachine learning model on the one or more regions highlighted by theuser, as explained previously. Once the user has highlighted the area ofinterest, e.g., using the mouse 104, the user can invoke the machinelearning model by entering a command via a suitable menu on the display,such as by clicking an “apply model” icon on the display or by using thekeyboard 106 to enter an appropriate command.

The machine learning model 110 can be resident in the workstation 100,or more typically it can be implemented by computing resource 106 on acomputer network 108. In one possible configuration, there are severalmachine learning models available. In the tissue pathology situation,the user may view the specimen at a higher magnification, e.g., 40×, anddesignate a new region at that magnification (e.g., region 40 in FIG.5B) and invoke a particular machine learning model trained to identifycancer cells at that magnification, whereas they may also view thespecimen and highlight a region of cells at low magnification, e.g.,10X, e.g., the region 50 of FIG. 5C, and a low magnification (10×)machine learning model is used for that inference task.

FIG. 7 is a flow chart showing one embodiment of the processingperformed in the computing environment of FIG. 6 to perform the method.A machine learning model performs inference on a 2D or 3D image data setof a patient or sample obtained therefrom. The model provides aprediction as to the disease state of a patient, and in one example anegative prediction of the disease state. The methodology indicated at200 includes the following steps:

Step 202: present on a display of a workstation the image of the patientor a sample obtained therefrom along with a risk score or classificationassociated with the prediction. The image is further augmented withhighlighting to indicate one or more regions in the image which affectedthe prediction produced by the machine learning model.

Step 204: provide a tool by which the user may highlight one or moreregions of the image which the user deems to be suspicious for thedisease state. The user invokes the tools to thereby highlight the oneor more regions, and such input is received by the workstation.

Step 206: perform inference on the highlighted one or more regions withthe machine learning model.

Step 208: present the results of the inference on the one or moreregions to the user via the display.

Optional step 210: present an updated risk score for the overall 2Dimage/3D volume.

The process can loop back as indicated at step 214 and steps 204, 206and 208, and 210 can repeat; this loop applies to the situation wherethe user specifies additional regions, as explained in FIGS. 5A-5C.

As will be apparent from FIGS. 2-6, it will be apparent that aninterface for a workstation has been described, which is configured tofacilitate human understanding of a machine learning model providing aprediction as to the disease state of a patient from a 2D or 3D image ofthe patient or a sample obtained therefrom. The machine learning modelproduces a prediction of the disease state from the image, e.g., anegative prediction such as “benign.” The interface includes a display(102, FIG. 6) for displaying the image of the patient or a sampleobtained therefrom along with a risk score or classification associatedwith the prediction. The image is further augmented with highlighting toindicate one or more regions in the image which affected the predictionproduced by the machine learning model. The interface includes a tool(e.g., mouse 104) by which the user may highlight on the display one ormore regions of the image, e.g., regions which the user deems to besuspicious for the disease state, wherein the user invokes the tools tothereby highlight the one or more regions, as explained in conjunctionwith FIGS. 3, 4 and 5A-5C.

Further Considerations

The method could be used to further examine an image even after themachine learning model has are already classified the specimen as apositive. An operator may suspect that there is another lesion worthreporting (either more or less severe) and would want the model toexplicitly examine it. Therefore, the method proceeds with the steps ofhighlighting the additional region, initiating model inference, and thengenerating the results of the inference and presenting it on theworkstation display.

The above description focuses on classification+localization problems,but the same method could be used in other ways, for example insegmentation and regression problems.

A. Segmentation

For example, an ultrasound image is presented on the workstation and themachine learning model is used to identify a prostate on the ultrasoundimage. An operator viewing the image sees a segmentation mask outlinesurrounding the prostate, and may suspect that some tissue that was notmarked within the mask also belonged to the prostate. The userhighlights this additional area and initiates model inference withkeyboard or mouse action. The model then either explains that thisregion is actually urethra, for example, or the model “agrees” to addthat region to the segmentation mask.

B. Regression Problems

For example, a machine learning model may be configured to answer aregression problem, such as “What is the bone age of a patient imagedwith an x-ray?” An X-ray of a bone along with the prediction of themodel is presented on the workstation display. The operator suspectsthat a certain region indicates a higher age, highlights it, initiatesinference, and the model updates its prediction accordingly. Thisgeneral procedure can of course be applied to other types of regressionproblems; the bone age example is offered by way of illustration and notlimitation.

The teachings also have applications in other areas, such as metallurgy,parts inspection, semiconductor manufacturing, and others, where amachine learning localization model is making a prediction regarding anobject based on an input image data set. For example, the prediction isnegative (e.g., no defect is present, or no undesirable impurity ispresent in a metallurgical sample), and the user seeks to query themodel further. The method follows the same basic approaches as describedabove:

a) present on a display of a workstation the image of the object alongwith a score or classification associated with the prediction. The imageis further augmented with highlighting to indicate one or more regionsin the image which affected the prediction produced by the machinelearning model;

b) provide a tool by which the user may highlight one or more additionalregions of the image which the user deems to be of further interestrelative to the prediction (such as for example, potential defects,impurities, etc.), wherein the user invokes the tools to therebyhighlight the one or more regions;

c) subject the highlighted one or more regions to inference by themachine learning model; and

d) present the results of the inference on the one or more regions tothe user via the display.

The appended claims are offered by way of further description of thedisclosed methods, workstation and user interface.

I claim:
 1. A method for assessing a machine learning model providing aprediction as to the disease state of a patient from a 2D or 3D image,comprising the steps of: a) presenting an image with a risk score orclassification associated with the prediction, wherein the image isfurther augmented with highlighting to indicate one or more regions inthe image which affected the prediction produced by the machine learningmodel; b) providing a user interface tool for highlighting one or moredifferent regions of the image, c) receiving a user input highlightingone or more different regions of the image; d) subjecting thehighlighted one or more different regions to inference by the machinelearning model; and e) presenting the results of the inference on theone or more different regions to the user via the display.
 2. The methodof claim 1, further comprising the steps of receiving user inputhighlighting still further different regions of the image and performingsteps d) and e) for such still further different regions.
 3. The methodof claim 1, wherein the prediction is associated with a risk score, andwherein step e) comprises the step of generating a new risk score. 4.The method of claim 1, wherein the machine learning model comprises aconvolutional neural network trained to recognize the presence of cancercells in the image.
 5. The method of claim 1, wherein the imagecomprises a magnified digital image of a tissue specimen.
 6. The methodof claim 1, wherein the image comprises a 2D radiological image.
 7. Themethod of claim 6, wherein the image is selected from the group ofimages consisting of a mammogram and a chest X-ray.
 8. The method ofclaim 1, wherein the image comprises a 3D radiological image obtainedfrom either nuclear magnetic resonance (NMR) or computed tomography(CT).
 9. The method of claim 1, wherein the tool comprises a mouseassociated with the display.
 10. The method of claim 1, wherein thedisplay is touch sensitive and wherein the tool comprises graphicsprocessing which record positions on the display which are toucheddirectly or indirectly by the user and translate such positions tolocations within the image.
 11. A workstation configured to assess amachine learning model providing a prediction as to the disease state ofa patient from a 2D or 3D image of the patient or a sample obtainedtherefrom, wherein the machine learning model produces a prediction ofthe disease state from the image, wherein the workstation comprises: a)display for displaying the image of the patient or a sample obtainedtherefrom along with a risk score or classification associated with theprediction, wherein the image is further augmented with highlighting toindicate one or more regions in the image which affected the predictionproduced by the machine learning model; b) a user interface tool bywhich the user may highlight on the display one or more differentregions of the image, wherein the user invokes the tool to therebyhighlight the one or more different regions; wherein the display isfurther configured to present the results of inference performed by themachine learning model on the one or more different regions highlightedby the user.
 12. The workstation of claim 11, wherein the workstationfurther comprises a processing unit in communication with the displayand wherein the machine learning model is implemented in the processingunit.
 13. The workstation of claim 11, wherein the machine learningmodel comprises a convolutional neural network trained to recognize thepresence of cancer in the image.
 14. The workstation of claim 11,wherein the image comprises a magnified digital image of a tissuespecimen.
 15. The workstation of claim 11, wherein the image comprises a2D radiological image.
 16. The workstation of claim 15, wherein theimage is selected from the group of images consisting of a mammogram anda chest X-ray.
 17. The workstation of claim 11, wherein the imagecomprises a 3D radiological image obtained from either nuclear magneticresonance or computed tomography.
 18. The workstation of claim 11,wherein the tool comprises a mouse associated with the display.
 19. Theworkstation of claim 11, wherein the display is touch sensitive andwherein the tool comprises graphics processing which record positions onthe display which are touched directly or indirectly by the user andtranslate such positions to locations within the image.
 20. An interfacefor a workstation configured to assess a machine learning modelproviding a prediction as to the disease state of a patient from a 2D or3D image of the patient or a sample obtained therefrom, wherein themachine learning model produces a prediction of the disease state fromthe image, wherein the interface comprises: a display for displaying theimage of the patient or a sample obtained therefrom along with a riskscore or classification associated with the prediction, wherein theimage is further augmented with highlighting to indicate one or moreregions in the image which affected the prediction produced by themachine learning model; and a user interface tool by which the user mayhighlight on the display one or more different regions of the imagewhich the user deems to be suspicious for the disease state, wherein theuser invokes the tools to thereby highlight the one or more differentregions.